{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activation Maximization on MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets build the mnist model and train it for 5 epochs. It should get to about ~99% test accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 6s - loss: 0.2346 - acc: 0.9279 - val_loss: 0.0552 - val_acc: 0.9817\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 5s - loss: 0.0845 - acc: 0.9756 - val_loss: 0.0363 - val_acc: 0.9881\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 5s - loss: 0.0620 - acc: 0.9819 - val_loss: 0.0336 - val_acc: 0.9897\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 5s - loss: 0.0533 - acc: 0.9839 - val_loss: 0.0317 - val_acc: 0.9895\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 5s - loss: 0.0436 - acc: 0.9865 - val_loss: 0.0295 - val_acc: 0.9910\n",
      "Test loss: 0.0295144059571\n",
      "Test accuracy: 0.991\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import keras\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Dropout, Flatten, Activation, Input\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 5\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "                 activation='relu',\n",
    "                 input_shape=input_shape))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax', name='preds'))\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adam(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          verbose=1,\n",
    "          validation_data=(x_test, y_test))\n",
    "\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dense Layer Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize activation over final dense layer outputs, we need to switch the `softmax` activation out for `linear` since gradient of output node will depend on all the other node activations. Doing this in keras is tricky, so we provide `utils.apply_modifications` to modify network parameters and rebuild the graph.\n",
    "\n",
    "If this swapping is not done, the results might be suboptimal. We will start by swapping out 'softmax' for 'linear' and compare what happens if we dont do this at the end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start by visualizing input that maximizes the output of node 0. Hopefully this looks like a 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd935d14a50>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2/b5x0OivIil73vgwT11n/GjkXe/96MJRa/wk2+d/sX+LNffhgdfDZ23S3gdlqd/e2Kh1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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9341a45d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from vis.visualization import visualize_activation\n",
    "from vis.utils import utils\n",
    "from keras import activations\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (18, 6)\n",
    "\n",
    "# Utility to search for layer index by name. \n",
    "# Alternatively we can specify this as -1 since it corresponds to the last layer.\n",
    "layer_idx = utils.find_layer_idx(model, 'preds')\n",
    "\n",
    "# Swap softmax with linear\n",
    "model.layers[layer_idx].activation = activations.linear\n",
    "model = utils.apply_modifications(model)\n",
    "\n",
    "# This is the output node we want to maximize.\n",
    "filter_idx = 0\n",
    "img = visualize_activation(model, layer_idx, filter_indices=filter_idx)\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Hmm, it sort of looks like a 0, but not as clear as we hoped for. Activation maximization is notorious because regularization parameters needs to be tuned depending on the problem. Lets enumerate all the possible reasons why this didn't work very well.\n",
    "    \n",
    "- The input to network is preprocessed to range (0, 1). We should specify `input_range = (0., 1.)` to constrain the input to this range.\n",
    "- The regularization parameter default weights might be dominating activation maximization loss weight. One way to debug this is to use `verbose=True` and examine individual loss values.\n",
    "\n",
    "Lets do these step by step and see if we can improve it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debugging step 1: Specifying input_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd93360dfd0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda1b1312d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = visualize_activation(model, layer_idx, filter_indices=filter_idx, input_range=(0., 1.))\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better but still seems noisy. Lets examining the losses with `verbose=True` and tuning the weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debugging step 2: Tuning regularization weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the issues with activation maximization is that the input can go out of the training distribution space. Total variation and L-p norm are used to provide some hardcoded image priors for natural images. For example, Total variation ensures that images are blobber and not scattered. Unfotunately, sometimes these losses can dominate the main `ActivationMaximization` loss.\n",
    "\n",
    "Lets see what individual losses are, with `verbose=True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 1, named_losses: [('ActivationMax Loss', 0.085303515),\n",
      " ('L-6.0 Norm Loss', 0.019828862),\n",
      " ('TV(2.0) Loss', 0.095951974)], overall loss: 0.201084345579\n",
      "Iteration: 2, named_losses: [('ActivationMax Loss', -2.3290093),\n",
      " ('L-6.0 Norm Loss', 0.17779008),\n",
      " ('TV(2.0) Loss', 507.10727)], overall loss: 504.956054688\n",
      "Iteration: 3, named_losses: [('ActivationMax Loss', -128.49548),\n",
      " ('L-6.0 Norm Loss', 0.19667891),\n",
      " ('TV(2.0) Loss', 164.45709)], overall loss: 36.1582946777\n",
      "Iteration: 4, named_losses: [('ActivationMax Loss', -250.48241),\n",
      " ('L-6.0 Norm Loss', 0.19896419),\n",
      " ('TV(2.0) Loss', 178.49908)], overall loss: -71.784362793\n",
      "Iteration: 5, named_losses: [('ActivationMax Loss', -355.06989),\n",
      " ('L-6.0 Norm Loss', 0.23281187),\n",
      " ('TV(2.0) Loss', 168.41599)], overall loss: -186.421081543\n",
      "Iteration: 6, named_losses: [('ActivationMax Loss', -434.47665),\n",
      " ('L-6.0 Norm Loss', 0.27569067),\n",
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      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd9332cbe90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd93362e050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = visualize_activation(model, layer_idx, filter_indices=filter_idx, input_range=(0., 1.), verbose=True)\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, `ActivationMax Loss` is not bouncing bouncing around and converging? Perhaps we could get that loss to be lower by reducing weights of other losses that might be dominating the overall loss being minimized. \n",
    "\n",
    "The simplest way to tune these weights is to first start with `0.` weights for all regularization losses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 1, named_losses: [('ActivationMax Loss', 0.2693989)], overall loss: 0.269398897886\n",
      "Iteration: 2, named_losses: [('ActivationMax Loss', -6.6923928)], overall loss: -6.69239282608\n",
      "Iteration: 3, named_losses: [('ActivationMax Loss', -225.87207)], overall loss: -225.872070312\n",
      "Iteration: 4, named_losses: [('ActivationMax Loss', -462.48257)], overall loss: -462.482574463\n",
      "Iteration: 5, named_losses: [('ActivationMax Loss', -689.16077)], overall loss: -689.160766602\n",
      "Iteration: 6, named_losses: [('ActivationMax Loss', -889.75427)], overall loss: -889.754272461\n",
      "Iteration: 7, named_losses: [('ActivationMax Loss', -1073.9482)], overall loss: -1073.94824219\n",
      "Iteration: 8, named_losses: [('ActivationMax Loss', -1242.8525)], overall loss: -1242.85253906\n",
      "Iteration: 9, named_losses: [('ActivationMax Loss', -1402.7773)], overall loss: -1402.77734375\n",
      "Iteration: 10, named_losses: [('ActivationMax Loss', -1558.7063)], overall loss: -1558.70629883\n",
      "Iteration: 11, named_losses: [('ActivationMax Loss', -1701.7627)], overall loss: -1701.76269531\n",
      "Iteration: 12, named_losses: [('ActivationMax Loss', -1840.2097)], overall loss: -1840.2097168\n",
      "Iteration: 13, named_losses: [('ActivationMax Loss', -1975.5297)], overall loss: -1975.52966309\n",
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     "data": {
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3FOrXsu52E2bzU0rTBnoffY3rOfi8Ua4r13Pg8TIIAGSAsgaADOwrZT17oDfQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd935e66110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = visualize_activation(model, layer_idx, filter_indices=filter_idx, input_range=(0., 1.), \n",
    "                           tv_weight=0., lp_norm_weight=0., verbose=True)\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does indeed go to much lower values, but the image looks less natural. Let's try varous range of total variation weights to enforce naturalness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9331519d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd932eb3d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Rewv1a1nvcRFmC1JKswd6HX2N+zn4HCj3lfs58HgZBAAyQFkDQAb2l7K+caAX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\nIAOUNQBkgLIGgAxQ1gCQAcoaADLw/wHLf+RQt81fTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd932c31c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JX5T0ort/bs5frapjOt92Xs7HdEUXxbTfFvPfJBUlPeTu/2XFJrOEzOxanbub\nlqSSpK+ulm01s69JulPnupWNSvozSd+S9Iik7TrXZfFD7p71L+fm2c47de5/l13SYUkPzHldN0tm\ndoekv5P0vKRW++FP6tzruavmmF5gO+/TZXpMWcEIABngF4wAkAHCGgAyQFgDQAYIawDIAGENABkg\nrAEgA4Q1AGSAsAaADPx/oDJoDoBcXjEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9329a9ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd93273fc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for tv_weight in [1e-3, 1e-2, 1e-1, 1, 10]:\n",
    "    # Lets turn off verbose output this time to avoid clutter and just see the output.\n",
    "    img = visualize_activation(model, layer_idx, filter_indices=filter_idx, input_range=(0., 1.), \n",
    "                               tv_weight=tv_weight, lp_norm_weight=0.)\n",
    "    plt.figure()\n",
    "    plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how total variation loss is enforcing blobbiness. In this case the default value of `tv_weight=10` seems to work very well. The point of this exercise was to show how weights can be tuned.\n",
    "\n",
    "Lets visualize all other output categories and see what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AChDWAFABwhoAKkBYA0AF1rc3SISUWY+f6feRWbcvKbZOpOoz83aij4gkjT9c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\ni1WYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9332fcf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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H/lR8n1WmYrUhIrVHLFj/IPz23Unvv1OL1YXwUqw2SGV0Or3vamws5WZsH3Xq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\nDQAZ+P8CULDqOCqb5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd932043a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DhDUAZICwBoAMENYAkAHCGgAyQFgDQAYIawDIAGENABmY0+XmblKjkr4M0wM/ShqLgktq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\nfwIkYgZPuaI7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd931d7c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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19wfNbLWkB8zszuK+P3D3T/QuPQCAlFCs3X2PpD3F5REze0zSpl4nBgD4odA5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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd931a26650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AMkBYA0AGCGsAyABhDQAZIKwBIAOENQBkgLAGgAwQ1gCQAcIaADJAWANABha0\nN4iXTbXB9J4Jkbd+n1wWexv6ci3WR6A0GqgPtihQsH9HqDdIV+z1uDwRm0p5LH3ukf4qkjRxWmy7\nlGvpDxDt9VEZi+3UriejB0G6ZiW2XWqn94fqy0cn02tH02sladm2eqh+YlVPeu3y2AFWGwj2B2qm\n79PQPkocljNrAMjArGFtZuvN7FYze8DM7jezdxa3X2Vmu8zs3uLfqzs/XQA4NaVcBqlLere732Nm\ng5LuNrNbiq/9pbt/qHPTAwBICWHt7nsk7Sk+HjazByWt6/TEAAA/FbpmbWZPl/Q8SXcVN73DzH5g\nZteZ2Yp5nhsAoJAc1mY2IOlGSe9y9yFJH5G0QdImtc68PzzN/baY2VYz21qbGJmHKQPAqScprM2s\nqlZQf8Ldb5Ikd9/n7g13b0r6qKQLp7qvu1/j7pvdfXO1O/YnRACAlpS/BjFJ10p60N2vbrt9bVvZ\n6yXdN//TAwBIaX8N8mJJb5H0QzO7t7jtfZKuMLNNav1J93ZJb+/IDAEASX8Ncqemfh/gr8z/dAAA\nU2EFIwBkYEF7g8gkD7w8TA4EioOtGMxjvSEagR4b5WYjNplarNwDbQcqY7G5VMZi/RV696fXjq6N\n9WIYXxUql5fT91HfvtjY1WAvke6h9HoLHrse7SUTPCVrLOtKrrVA/x4p3qsm8n1tsV2kSqTfj6R6\nb/p2rwR6D6Xuf86sASADhDUAZICwBoAMENYAkAHCGgAyQFgDQAYIawDIAGENABkgrAEgA4Q1AGRg\nQZebu0mNavqSTQu89Xt0qWmjK7ZktzzeubnIY8teS5PpS8gb1djrcXRZda0/fXl6dFn1+Jp6qL40\nmX44e2xVfVipEXyyARYcuxlYhi9J5bH07V4frIbG9lLnlspXJmLH7mQltl0s0Llh9a27kmu3DaX1\nm+DMGgAyQFgDQAYIawDIAGENABkgrAEgA4Q1AGSAsAaADBDWAJABwhoAMkBYA0AGCGsAyIB5sC/F\nnB7M7AlJj0/xpVWSDizYRBYPz3PpOVWeK8+zc85y9zNmK1rQsJ52EmZb3X3zYs+j03ieS8+p8lx5\nnouPyyAAkAHCGgAycLKE9TWLPYEFwvNcek6V58rzXGQnxTVrAMDMTpYzawDADBY1rM3sYjN7yMy2\nmdl7F3MunWZm283sh2Z2r5ltXez5zBczu87M9pvZfW23rTSzW8zs4eL/FYs5x/kwzfO8ysx2Ffv0\nXjN79WIYTdmWAAACjUlEQVTOcT6Y2Xozu9XMHjCz+83sncXtS2qfzvA8T9p9umiXQcysLOnHkl4h\naaek70q6wt0fWJQJdZiZbZe02d2X1N+qmtlLJR2V9DF3P7+47b9LOuTuHyxehFe4+x8u5jznaprn\neZWko+7+ocWc23wys7WS1rr7PWY2KOluSZdJequW0D6d4XlerpN0ny7mmfWFkra5+6PuPinp05Iu\nXcT54AS4++2SDh1386WSbig+vkGtb4KsTfM8lxx33+Pu9xQfD0t6UNI6LbF9OsPzPGktZlivk7Sj\n7fOdOsk31hy5pK+b2d1mtmWxJ9Nha9x9T/HxXklrFnMyHfYOM/tBcZkk60sDxzOzp0t6nqS7tIT3\n6XHPUzpJ9ym/YFw4L3H3X5T0Kkm/W/xYveR56zrbUv2To49I2iBpk6Q9kj68uNOZP2Y2IOlGSe9y\n96H2ry2lfTrF8zxp9+lihvUuSevbPn9qcduS5O67iv/3S/q8WpeBlqp9xTXBY9cG9y/yfDrC3fe5\ne8Pdm5I+qiWyT82sqlaAfcLdbypuXnL7dKrneTLv08UM6+9KOtfMzjazLklvknTzIs6nY8ysv/gl\nhsysX9IrJd03872ydrOkK4uPr5T0xUWcS8ccC6/C67UE9qmZmaRrJT3o7le3fWlJ7dPpnufJvE8X\ndVFM8Wcx/0NSWdJ17v5nizaZDjKzZ6h1Ni1JFUmfXCrP1cw+JekitbqV7ZP0fklfkPRZSU9Tq8vi\n5e6e9S/npnmeF6n147JL2i7p7W3XdbNkZi+RdIekH0pqFje/T63ruUtmn87wPK/QSbpPWcEIABng\nF4wAkAHCGgAyQFgDQAYIawDIAGENABkgrAEgA4Q1AGSAsAaADPx/5dh+4H79mZ4AAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9316cfad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9313fdf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3VBqxsaMi41sztmPqLwX7lPSm10f3eVToPhqP7Zdw/47AbvTgKVO410e0Z0YH\ne2CYd27yrWCvj6hI/6HofZSCM2sAyMC0YW1ma83sdjN7yMweNLPPlJdfaWZbzey+8t95nZ8uABye\nUl4GaUi61N3vNbMlku4xs1vL677k7l/o3PQAAFJCWLv7Nknbyq8HzexhScd2emIAgFeEXrM2sxMk\nnSnp7vKiT5vZ/WZ2rZmtmOW5AQBKyWFtZgOSbpR0ibvvlfRlSSdJWqfizPuLU9zuYjPbYGYbmsND\nszBlADj8JIW1mdVUBPXX3f07kuTuO9y96e4tSV+RdNZkt3X3q919vbuvr/b2z9a8AeCwkvJuEJN0\njaSH3f2qtsvXtJV9UNLG2Z8eAEBKezfI2yR9XNIDZnZfedkVki4ws3UqlqNskvSpjswQAJD0bpAf\na/L1WD+Y/ekAACbDCkYAyMCc9waJrK8PrfXvYD8DKdYvoTIWHDt4L4wPRBpPxMaO9sDo2p9e26zH\nxl5Qor0eOtgbRIHHkCQp2DMjcgxEj5dIb6Co8FyiPVMCc6c3CAAcpghrAMgAYQ0AGSCsASADhDUA\nZICwBoAMENYAkAHCGgAyQFgDQAYIawDIwJwvN+/YctMOLgeWgstHg2O3gvdCZHlydTQ2dmU8OJfA\n/Rkdu9nTuXprRdcaBw+whbRkOzh+aLl5cOm712P7PbIkvJNL2aXYflm2qZFcWx1NO7Y4swaADBDW\nAJABwhoAMkBYA0AGCGsAyABhDQAZIKwBIAOENQBkgLAGgAwQ1gCQAcIaADJgHu15MJMfZva8pGcm\nuepISS/M2UTmD9u5+Bwu28p2ds7x7r5quqI5DespJ2G2wd3Xz/c8Oo3tXHwOl21lO+cfL4MAQAYI\nawDIwEIJ66vnewJzhO1cfA6XbWU759mCeM0aAHBwC+XMGgBwEPMa1mZ2rpk9amZPmNnl8zmXTjOz\nTWb2gJndZ2Yb5ns+s8XMrjWznWa2se2ylWZ2q5k9Xv6/Yj7nOBum2M4rzWxreZ/eZ2bnzeccZ4OZ\nrTWz283sITN70Mw+U16+qO7Tg2zngr1P5+1lEDOrSnpM0jmStkj6maQL3P2heZlQh5nZJknr3X1R\nvVfVzN4haZ+kr7n76eVl/1HSLnf/fPkkvMLdf28+5zlTU2znlZL2ufsX5nNus8nM1kha4+73mtkS\nSfdI+oCkT2gR3acH2c4Pa4Hep/N5Zn2WpCfc/Sl3H5N0g6Tz53E+OATufqekXQdcfL6k68uvr1fx\nIMjaFNtFPdrvAAAB2ElEQVS56Lj7Nne/t/x6UNLDko7VIrtPD7KdC9Z8hvWxkja3fb9FC3xnzZBL\n+qGZ3WNmF8/3ZDpstbtvK7/eLmn1fE6mwz5tZveXL5Nk/dLAgczsBElnSrpbi/g+PWA7pQV6n/IH\nxrnzdnf/h5LeK+m3yl+rFz0vXmdbrG85+rKkkyStk7RN0hfndzqzx8wGJN0o6RJ339t+3WK6TyfZ\nzgV7n85nWG+VtLbt++PKyxYld99a/r9T0k0qXgZarHaUrwlOvDa4c57n0xHuvsPdm+7ekvQVLZL7\n1MxqKgLs6+7+nfLiRXefTradC/k+nc+w/pmkk83s9WZWl/RRSbfM43w6xsz6yz9iyMz6Jb1H0saD\n3yprt0i6sPz6Qkk3z+NcOmYivEof1CK4T83MJF0j6WF3v6rtqkV1n061nQv5Pp3XRTHl22L+s6Sq\npGvd/Y/nbTIdZGYnqjiblqQuSd9YLNtqZt+UdLaKbmU7JH1O0nclfUvS61R0Wfywu2f9x7kptvNs\nFb8uu6RNkj7V9rpulszs7ZLukvSApFZ58RUqXs9dNPfpQbbzAi3Q+5QVjACQAf7ACAAZIKwBIAOE\nNQBkgLAGgAwQ1gCQAcIaADJAWANABghrAMjA/wcv60DXPulX0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9310ba410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd930d61890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zLsqiPVMC/T4i2zC1YQpH1gCQgSnD2szWmNktZvagmT1gZu8pL7/azDab2T3l\nv4vaP10AODql/EJVl/Red7/bzBZJusvMbi6v+4S7f6x90wMASAlh7e5bJW0tv+43s4ckndjuiQEA\nfil0ztrMTpZ0nqQ7y4veaWb3mtn1ZhZ79wgAkCw5rM2sT9KXJV3p7gckfUrSaZLWqjjy/vgkt7vC\nzNab2frGwOAMTBkAjj5JYW1mnSqC+rPu/hVJcvft7t5w96akT0s6f6Lbuvu17r7O3ddV+3pnat4A\ncFRJ+WsQk3SdpIfc/ZqWy1v/OPj1ku6f+ekBAKS0vwZ5oaS3SrrPzO4pL7tK0qVmtlbFn3RvkPSO\ntswQAJD01yA/UPG55ON9e+anAwCYCCsYASADs9sbpCKpK9AHoX2tQZLX4x/W0JXg4MOxh2HLgcXJ\ntV/c8bzQ2L+19PFQ/Z56+pvGu2uxN5i3DMf6d9Sb6X0qdg3H5jJciz1GtUb6XCrB/aU+EuzHEdwd\nbSzQ1yLYusPSW6ZIkjxyV4P3MzS2Yn1KQmMnDsuRNQBkgLAGgAwQ1gCQAcIaADJAWANABghrAMgA\nYQ0AGSCsASADhDUAZICwBoAMzO5y86jIS0nko98lmQfrIx9bH1wm792xNbgjI53ptY3YQ7x1LLbE\nu2Lpa3x3jfaFxh6qd4XqI6LdBkZr6dtckjzwAxr12Lpnj+yLCu67QRZ83nk1tuW9jYeT4aXvgbk8\n88ofJdfu9rQPZeHIGgAyQFgDQAYIawDIAGENABkgrAEgA4Q1AGSAsAaADBDWAJABwhoAMkBYA0AG\nCGsAyIB5pInBdH+Y2U5JT05w1bGSds3aROYO93P+OVruK/ezfU5y9+OmKprVsJ50Embr3X3dXM+j\n3bif88/Rcl+5n3OP0yAAkAHCGgAycKSE9bVzPYFZwv2cf46W+8r9nGNHxDlrAMChHSlH1gCAQ5jT\nsDazC83sETN7zMw+MJdzaTcz22Bm95nZPWa2fq7nM1PM7Hoz22Fm97dcttzMbjazR8v/l83lHGfC\nJPfzajPbXD6m95jZRXM5x5lgZmvM7BYze9DMHjCz95SXz6vH9BD384h9TOfsNIiZVSX9XNIrJG2S\n9BNJl7r7g3MyoTYzsw2S1rn7vPpbVTN7saQBSX/v7ueWl/03SXvc/aPli/Ayd//TuZzndE1yP6+W\nNODuH5vLuc0kM1slaZW7321miyTdJel1kt6mefSYHuJ+XqIj9DGdyyPr8yU95u6Pu/uYpC9IungO\n54PD4O7e1ICPAAAB7ElEQVS3S9oz7uKLJd1Yfn2jiidB1ia5n/OOu29197vLr/slPSTpRM2zx/QQ\n9/OINZdhfaKkjS3fb9IRvrGmySV918zuMrMr5noybbbS3beWX2+TtHIuJ9Nm7zSze8vTJFmfGhjP\nzE6WdJ6kOzWPH9Nx91M6Qh9T3mCcPS9y9+dIepWkPy5/rZ73vDjPNl//5OhTkk6TtFbSVkkfn9vp\nzBwz65P0ZUlXuvuB1uvm02M6wf08Yh/TuQzrzZLWtHy/urxsXnL3zeX/OyTdpOI00Hy1vTwnePDc\n4I45nk9buPt2d2+4e1PSpzVPHlMz61QRYJ9196+UF8+7x3Si+3kkP6ZzGdY/kXS6mZ1iZl2S3izp\n63M4n7Yxs97yTQyZWa+kV0q6/9C3ytrXJV1Wfn2ZpK/N4Vza5mB4lV6vefCYmplJuk7SQ+5+TctV\n8+oxnex+HsmP6Zwuiin/LOavJFUlXe/ufzlnk2kjMztVxdG0JHVI+tx8ua9m9nlJF6joVrZd0ock\nfVXSFyU9Q0WXxUvcPes35ya5nxeo+HXZJW2Q9I6W87pZMrMXSbpD0n2SmuXFV6k4nztvHtND3M9L\ndYQ+pqxgBIAM8AYjAGSAsAaADBDWAJABwhoAMkBYA0AGCGsAyABhDQAZIKwBIAP/HzcoSPNABGfr\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd930a0bd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YR8R2Sduby3tt3yTprHEnBgD4sdQ5a9sPlPRoSV9sbnqF7a/ZvtL25mXODQDQ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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9329df2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for output_idx in np.arange(10):\n",
    "    # Lets turn off verbose output this time to avoid clutter and just see the output.\n",
    "    img = visualize_activation(model, layer_idx, filter_indices=output_idx, input_range=(0., 1.))\n",
    "    plt.figure()\n",
    "    plt.title('Networks perception of {}'.format(output_idx))\n",
    "    plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty cool. Its amazing that we can even generate an input image via backprop! \n",
    "\n",
    "Obviously you can tune the visualizations to look better by experimenting with `image_modifiers`, lp-norm weight etc. Basically, a regularizer is needed to enforce image naturalness prior to limit the input image search space. By this point, GANs should come to your mind. We could easily take a GAN trained on mnist and use discriminator loss as a regularizer. For using custom loss, you can use `visualize_activation_with_losses` API.\n",
    "\n",
    "Feel free to submit a PR if you try the GAN regularizer :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other fun stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The API to `visualize_activation` accepts `filter_indices`. This is generally meant for *multi label* classifiers, but nothing prevents us from having some fun. \n",
    "\n",
    "By setting `filter_indices=[1, 7]`, we can generate an input that the network thinks is both 1 and 7 simultaneously. Its like asking the network\n",
    "\n",
    "> Generate input image that you think is both 1 and a 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd92febccd0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9301c2dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = visualize_activation(model, layer_idx, filter_indices=[1, 7], input_range=(0., 1.))\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare this to the `1` generated above and you should be able to see the difference. Nifty indded!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations without swapping softmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As alluded at the beginning of the tutorial, we want to compare and see what happens if we didnt swap out softmax for linear activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r2/2SXiFp/5n299r+ju3LbB8zw2MDADSKw9r2UZKulPT+iHhc0qclvVjSGWod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\nAv8fVzixMMzR9toAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd936dca2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9r0ruL5v252rPZLgz96GBbJ2Srf3166Bk18uGvh2p9ut6l9Vum9mml654rlY7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd937a8ea50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lx2N4OFWfWVbb7dyy5/U7tqTqU0ufYyQ19iXzzk7VZ8Zftyu3BNvdRzU2F0naH/UfA+sG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\ngLAGgAIQ1gBQgP8P+DnpSekd1p8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda1b340990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9358c2c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oacjPZbn7De4+393nN2vGeI0bAI4qKZ8GMUk3SXrG3a+vun1mVbNVkp4c/+EB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\nwhoAMkBYA0AGCGsAyMD/B4+qj2NBXMfnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9355e9290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd93528d650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd934fb4bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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61/bdzW0fk3SJ7dMkhaRBSe/vyAgBAEWfBrlV0kSza66f/eEAACbCDEYAqMCc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\nAVma5ZWgts4HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd934c64f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mBwCQEsLa3fdJ2ld+fNDMHpVUzYVnAMCQQteszaxd0nskbS9v+qSZPWhmN5rZ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\nA4Q1AGSAsAaADPx/EwmctNIL+/AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd93491c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BoADTOt38OC+Ipe0XTdvzjaWVU3A39d6aar9y8dLcEySmycbQUK7r5HrZ0Ls11T6jlVPZ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\nA0ABCGsAKABhDQAFIKwBoACENQAU4P8D2tvGWHHxvvYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9345cc810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Swap linear back with softmax\n",
    "model.layers[layer_idx].activation = activations.softmax\n",
    "model = utils.apply_modifications(model)\n",
    "\n",
    "for output_idx in np.arange(10):\n",
    "    # Lets turn off verbose output this time to avoid clutter and just see the output.\n",
    "    img = visualize_activation(model, layer_idx, filter_indices=output_idx, input_range=(0., 1.))\n",
    "    plt.figure()\n",
    "    plt.title('Networks perception of {}'.format(output_idx))\n",
    "    plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not work! The reason is that maximizing an output node can be done by minimizing other outputs. Softmax is weird that way. It is the only activation that depends on other node output(s) in the layer."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
